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Language Modeling

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/msalhab96/Conformer

Conformer: Convolution-augmented Transformer for Speech Recognition

https://github.com/msalhab96/Listen-Attend-and-Spell

Listen, Attend and Spell

https://github.com/msalhab96/SpeeQ

Listen, Attend and Spell

https://github.com/msorkhpar/wiki-entity-summarization-preprocessor

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

https://github.com/mszpc/SoftMaskedBert

Spelling Error Correction with Soft-Masked BERT

https://github.com/mtanti/rnn-role

Where to put the Image in an Image Caption Generator

https://github.com/mthcom/hscore-dataset-pruning

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/muellerzr/CodeFest_2019

Regularizing and Optimizing LSTM Language Models

https://github.com/muennighoff/sgpt

Generative Representational Instruction Tuning

https://github.com/multi-swe-bench/multi-swe-bench

Training Software Engineering Agents and Verifiers with SWE-Gym

https://github.com/musixmatchresearch/umberto

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/mustafa-yavuz/chatbot-qa

End-To-End Memory Networks

https://github.com/mustafaaljadery/gemma-2b-10m

Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

https://github.com/muu4649/word2vec_attention

Long Short-Term Memory-Networks for Machine Reading

https://github.com/mv-lab/InstructIR

InstructPix2Pix: Learning to Follow Image Editing Instructions

https://github.com/mvenouziou/Project-Attention-Is-What-You-Get

Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet

https://github.com/my-other-github-account/llm-humaneval-benchmarks

Evaluating Large Language Models Trained on Code

https://github.com/myamamoto555/tf-bert

Semi-supervised Sequence Learning

https://github.com/myinnos/List-of-Machine-Learning-And-Statistics-Repositories

Learning Longer Memory in Recurrent Neural Networks

https://github.com/myinnos/Machine-Learning-And-Statistics-Repos

Learning Longer Memory in Recurrent Neural Networks

https://github.com/mzhaoshuai/slimclr

Protecting Copyrighted Material with Unique Identifiers in Large Language Model Training

https://github.com/n-waves/multifit

MultiFiT: Efficient Multi-lingual Language Model Fine-tuning

https://github.com/n-waves/poleval2018

Universal Language Model Fine-Tuning with Subword Tokenization for Polish

https://github.com/n4feng/ResponseQualityAssessment

Large language model validity via enhanced conformal prediction methods

https://github.com/nachiket273/lookahead_pytorch

On the Variance of the Adaptive Learning Rate and Beyond

https://github.com/nachiketaa/bert

Semi-supervised Sequence Learning

https://github.com/nageshsinghc4/deepwrap

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

https://github.com/nanowell/AdEMAMix-Optimizer-Pytorch

The AdEMAMix Optimizer: Better, Faster, Older

https://github.com/nanzhaogang/contrib/tree/master/application/recurrent-highway-network

Recurrent Highway Networks

https://github.com/narutomst/Auto-CNN-HSI-Classification

DARTS: Differentiable Architecture Search

https://github.com/nasib-ullah/video-captioning-models-in-Pytorch

Sequence to Sequence -- Video to Text

https://github.com/nathanlesage/so-classifier

Neural Text Generation from Structured Data with Application to the Biography Domain

https://github.com/naver-ai/jood

Jailbreaking Attack against Multimodal Large Language Model

https://github.com/naver-ai/simseek

Longformer: The Long-Document Transformer

https://github.com/nbansal90/bAbi_QA

Recurrent Neural Network Regularization

https://github.com/ncoop57/gpt-code-clippy

Language Models are Few-Shot Learners

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.